Integrating implementation science and intervention optimization
Bibliographic record
Abstract
BACKGROUND: Implementation scientists increasingly recognize the value of multiple strategies to improve the adoption, fidelity, and scale up of an evidence-based intervention (EBI). However, with this recognition comes the need for alternative and innovative methods to ensure that the package of implementation strategies work well within constraints imposed by the need for affordability, scalability, and/or efficiency. The aim of this article is to illustrate that this can be accomplished by integrating principles of intervention optimization into implementation science. METHOD: We use a hypothetical example to illustrate the application of the multiphase optimization strategy (MOST) to develop and optimize a package of implementation strategies designed to improve clinic-level adoption of an EBI for smoking cessation. RESULTS: We describe the steps an investigative team would take using MOST for an implementation science study. For each of the three phases of MOST (preparation, optimization, and evaluation), we describe the selection, optimization, and evaluation of four candidate implementation strategies (e.g., training, treatment guide, workflow redesign, and supervision). We provide practical considerations and discuss key methodological points. CONCLUSION: Our intention in this methodological article is to inspire implementation scientists to integrate principles of intervention optimization in their studies, and to encourage the continued advancement of this integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.292 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".